Updated the ancillary MODIS fPAR 8-day climatology used for fPAR gap-filling as an L4C model preprocessing step to reflect new MODIS Collection 6 fPAR inputs. The fPAR climatology is derived from a longer 14-year (2000-2014) MODIS record relative to the original 12-year (2000-2012) Collection 5 fPAR record used in Version 2 processing.

For each grid cell, a sine-curve-based seasonal fPAR climatology curve is now used to identify and screen anomalous 8-day fPAR variations in the preprocessor. This change reduces impacts of anomalous fPAR temporal variations that may not be captured by the MODIS fPAR product quality control (QC) flags, particularly during seasonal transitions at northern latitudes.

A minor bug fix to the post-processor was made to ensure that all grid cell no-data fill values are identified with a consistent -9999 notation; the prior Version 2 product erroneously assigned some no-data values as -999900.

Detailed Data Description

Parameter Description

This SMAP data product contains daily estimates of global ecosystem productivity, including net ecosystem exchange (NEE), gross primary production (GPP), heterotrophic respiration (Rh), and soil organic carbon (SOC), along with quality control metrics. The NEE of CO2 with the atmosphere is a fundamental measure of the balance between carbon uptake by vegetation GPP, and carbon losses through autotrophic respiration (Ra) and heterotrophic respiration (Rh). The sum of Ra and Rh defines the total ecosystem respiration rate (Rtot), which encompasses most of the annual terrestrial CO2 efflux to the atmosphere. All parameters are expressed in units of g C m-2 day-1. The CO2 flux state variable outputs are provided in SPL4CMDL files as eight vegetated land-cover classes called Plant Function Types (PFTs). For example, the CO2 flux state variable outputs are provided in NEE/nee_pft{1..8}_mean, GPP/gpp_pft{1..8}_mean, and RH/gpp_pft{1..8}_mean. The soil carbon pool state variable output are provided in SOC/soc_pft{1..8}_mean. Refer to Table 1 for descriptions of the eight PFTs.

Totals for each vegetated land class (i.e. count of vegetated 1 km grid cells contained within each 9 km grid cell) are provided in each SPL4CMDL file (QA/qa_count_pft{1..8}). Non-vegetated grid cells are determined by the union of specified vegetation PFT classes in Table 1 and availability of long-term MODIS fPAR (MOD15A2) for production of the fPAR climatology (refer to the Baseline Algorithm). Vegetated PFT grid cells lacking sufficient fPAR retrievals to produce the fPAR climatology and non-vegetated PFT grid cells with otherwise valid fPAR climatology are excluded from SPL4CMDL simulations and QA counts. QA counts are time-static and are therefore identical across files because the PFT classification does not change over the course of data generation within each SPL4CMDL version.

Users may use the QA count information to compute total non-vegetated 1 km grid cell coverage, compute percent coverage for each PFT, and account for non-vegetated regions when computing areal averages from SPL4CMDL state variables. For example, when computing the total GPP within a 9 km grid cell, a user would multiply the mean GPP (i.e. /GPP/gpp_mean in g C m-2 d-1) by the vegetated PFT total QA count (i.e. /QA/qa_count).

NEE

QA

RH

SOC

Metadata Fields

Includes all metadata that describe the full content of each file. For a description of all metadata fields for this product, refer to the Metadata Fields document.

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File Naming Convention

Files are named according to the following convention, which is described in Table 2:

SMAP_L4_C_MDL_yyyymmddThhmmss_VLMmmm_NNN.[ext]

For example:

SMAP_L4_C_mdl_20151007T000000_Vv3040_001.h5

Where:

Table 2. File Naming Conventions

Variable

Description

SMAP

Indicates SMAP mission data

L4_C_MDL

Indicates specific product (L4: Level-4; C: Carbon; MDL: Model)

yyyymmddThhmmss

Date/time in Universal Coordinated Time (UTC) of the first data element that appears in the product, where:

yyyymmdd

4-digit year, 2-digit month, 2-digit day

T

Time (delineates the date from the time, i.e. yyyymmddThhmmss)

hhmmss

2-digit hour, 2-digit minute, 2-digit second

VLMmmm

Science Version ID, where:

Variable

Description

V

Version

L

Launch Indicator (V: Validated Data)

M

1-Digit Major Version Number

mmm

3-Digit Minor Version Number

Example:Vv3040 indicates a Validated product with a version of 3.040. Refer to the SMAP Data Versions page for version information.

Note: The data product Science Version ID (example: Vv3040) consists of the first six characters of the data product Composite Release ID (CRID). The full CRID includes four additional digits that are to be found in individual granule metadata within the DataIdentifcation/DatasetIdentification/CompositeReleaseID field. These additional digits denote minor processing changes, such as runtime configuration and other minor changes that do not impact the science of the data product.

NNN

Number of times the file was generated under the same version for a particular date/time interval (002: 2nd time)

.[ext]

File extensions include:

.h5

HDF5 data file

.xml

XML Metadata file

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File Size

Each file is approximately 133 MB.

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Volume

The daily data volume is approximately 133 MB.

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Spatial Coverage

Coverage spans from 180°W to 180°E, and from approximately 85.044°N and 85.044°S.

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Spatial Resolution

Level-4 carbon model inputs include the following spatial resolutions:

Note that while this product has a 9 km spatial resolution, it also retains sub-grid scale heterogeneity information as determined from the 1 km resolution processing using MODIS PFT and fPAR inputs.

For more details regarding inputs used in the carbon model, refer to the Data Sources section.

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Projection and Grid Description

EASE-Grid 2.0

These data are provided on the global cylindrical EASE-Grid 2.0 (Brodzik et al. 2012). Each grid cell has a nominal area of approximately 9 x 9 km2 regardless of longitude and latitude.

EASE-Grid 2.0 has a flexible formulation. By adjusting a single scaling parameter, a family of multi-resolution grids that nest within one another can be generated. The nesting can be adjusted so that smaller grid cells can be tessellated to form larger grid cells. Figure 2 shows a schematic of the nesting to a resolution of 3 km (4872 rows x 11568 columns on global coverage), 9 km (1624 rows x 3856 columns on global coverage) and 36 km (406 rows x 964 columns on global coverage). Note that the grid used for this product has been adjusted using a scaling parameter in order to accommodate a resolution of 1 km.

This feature of perfect nesting provides SMAP data products with a convenient common projection for both high-resolution radar observations and low-resolution radiometer observations, as well as for their derived geophysical products.

SMAP Satellite and Processing Events

Due to instrument maneuvers, data downlink anomalies, data quality screening, and other factors, small gaps in the SMAP time series will occur. Details of these events are maintained on two master lists:

However, gaps in the SMAP time series do not affect this product. For the analytical variables, the ancillary MODIS fPAR 8-day climatology provides a fallback input source to help ensure there are no spatio-temporal gaps in the modeled data record.

Latencies

Each Level-4 file is a daily composite. Calculations for this product are conducted at a daily time step in order to provide the necessary precision for resolving dynamic boreal vegetation phenology and carbon cycles (Kimball et al. 2009, Kim et al. 2012).

III Due to the loss of the SMAP radar instrument and operational freeze/thaw (F/T) classification product, SPL4CMDL uses the GMAO GEOS-5-modeled TSURF parameter to define F/T conditions in the carbon model.

To address these limitations, the primary objectives of the SPL4CMDL product are to:

Determine NEE regional patterns and temporal behavior (daily, seasonal, and annual) to within the accuracy range of in situ tower measurement estimates of these
processes;

Link NEE estimates with component carbon fluxes (GPP and Rtot) and the primary environmental constraints to ecosystem productivity and respiration.

The SPL4CMDL algorithm supports carbon cycle science objectives by enabling detailed mapping and monitoring of spatial patterns and temporal dynamics of land-atmosphere CO2 exchange, and the underlying carbon fluxes and environmental drivers of these processes. The SPL4CMDL product also links SMAP land parameter measurements to global terrestrial CO2 exchange, including boreal ecosystems, reducing uncertainties about the "missing sink" on land for atmospheric CO2 .

Atmospheric transport model inversions of CO2 concentrations indicate that the Northern Hemisphere terrestrial biosphere is responsible for much of the recent terrestrial sink strength for atmospheric carbon (Dargaville et al. 2002). Variability in land-atmosphere CO2 exchange is strongly controlled by climatic fluctuations and disturbance, while uncertainty regarding the magnitude and stability of the sink are constrained by a lack of detailed knowledge on the response of underlying processes at regional scales (Denman et al. 2007, Houghton 2003).

The SPL4CMDL product enables quantification and mechanistic understanding of spatial and temporal variations in NEE over a global domain. NEE represents the primary measure of carbon (CO2) exchange between the land and atmosphere, and the SPL4CMDL product is directly relevant to a range of applications including regional mapping and monitoring of terrestrial carbon stocks and fluxes, climate and drought related impacts on vegetation productivity, and atmospheric transport model inversions of terrestrial source-sink activity for atmospheric CO2.

For more background information, refer to Section 2.3: Historical Perspective in the ATBD for this product.

Dynamic daily inputs to the SPL4CMDL algorithms include satellite optical infrared (IR) remote sensing MODIS-based fPAR, GEOS-5 surface meteorology (Rsw, Tmn, VPD) and associated SPL4SMGP soil moisture (SMrz) which provide primary inputs to a LUE algorithm to determine GPP, where Rsw is incoming shortwave solar radiation (MJ m-2 d-1); Tmn is minimum daily 2 m air temperature (°C), VPD is atmosphere vapor pressure deficit (Pa), and SMrz is the integrated surface to root zone (0-1 m depth) soil moisture (% Sat.). The SPL4CMDL dynamic inputs also include GEOS-5 surface temperature (Ts, °C), defined frozen temperature (F/T), constraints to GPP, and autotrophic respiration calculations. SMAP Level-4 surface soil moisture (≤ 5 cm depth) and soil temperature are used as primary drivers of the soil decomposition and Rh calculations. Static inputs to the SPL4CMDL algorithms include a global land cover classification, which is used to define the major plant functional types and associated biome-specific Biome Properties Look-Up Table (BPLUT) response characteristics for each vegetated grid cell within the product domain. The BPLUT parameters are defined for up to eight global vegetation (PFT) classes; the model parameters for each global PFT class were calibrated by optimizing carbon model NEE calculations against tower eddy covariance measurement-based daily NEE observations from global FLUXNET sites representing the major PFT classes (Baldocchi 2008). The land cover classification used for SPL4CMDL processing is consistent with the one used in the production of the fPAR inputs. All model inputs are available as satellite remote sensing derived products or from model (GEOS-5) analysis.

The resulting SPL4CMDL parameters enable characterization of spatial patterns and daily temporal fidelity in NEE, underlying carbon fluxes and SOC pools, and their primary environmental drivers. The resulting fine scale (1 km resolution) SPL4CMDL outputs are spatially aggregated to the coarser 9 km resolution final product grid by weighted linear averaging of outputs according to the fractional cover of individual PFT classes represented within each 9 km grid cell and defined by the underlying 1 km resolution MODIS PFT map. The sub-grid scale means from individual PFT classes are preserved for each 9 km grid cell, while proportional vegetation cover information is included in the product metadata, allowing the coarse resolution data to be decomposed into the relative contributions from individual PFT classes within each cell. These outputs are designed to facilitate improved algorithm and product accuracy over heterogeneous land cover areas, and product outputs that are more consistent with the mean sampling footprint of most tower CO2 flux measurement sites (Baldocchi 2008, Chen et al. 2012).

Algorithm Options

The SPL4CMDL baseline product contains various processing options that are implemented in the algorithm preprocessing stage for handling of the daily model inputs. These processing options are distinct from other options that are more internal to the model algorithms (Kimball et al. 2014). Two major preprocessing options are used in the SPL4CMDL product, including use of estimated clear-sky fPAR inputs for missing or lower quality MODIS fPAR inputs, and use of GEOS-5 surface temperature fields to estimate frozen temperature constraints to the GPP calculations instead of SMAP radar F/T-defined constraints. The use of these preprocessing options are noted in the SPL4CMDL product bit flags as defined in Table 7 of this document and on the Data Fields page.

For more information regarding algorithm options, refer to the ATBD, for this product.

Ancillary Data

Ancillary data required as input for the algorithms are summarized in Table 4. For in-depth information on ancillary data, refer to the ATBD, Section 3.2: Ancillary Data Requirements.

For more information regarding the algorithm, refer to the ATBD for this product.

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Processing Steps

Written by the University of Montana's Numerical Terradynamic Simulation Group (NTSG), the SPL4CMDL science code was transferred from NTSG to the NASA Global Modelling and Assimilation Office (GMAO) for translation and implementation as operational code in conjunction with SMAP Level-4 soil moisture production within the GMAO Level-4 SMAP Science Data Processing System (SDS).

The ingested data are then inspected for retrievability criteria according to input data quality, ancillary data availability, and land cover conditions.

Two pre-processor codes, one for fPAR data and one for global meteorology data, are then executed each day to temporally aggregate and resample these respective inputs for use by the baseline algorithm software. When retrievability criteria are met, the production software invokes the baseline retrieval algorithm to generate the daily carbon model outputs.

SPL4CMDL calculations are conducted at 1 km resolution, benefiting from finer scale (500 m) MODIS fPAR and land cover inputs. The simulations have also been conducted in a consistent global EASE-Grid 2.0 projection format. Model simulations for each 1 km grid cell are conducted using the corresponding (nearest-neighbor) 9 km resolution SMAP Level-4 Soil Moisture and GEOS-5 inputs. The MODIS (MOD/MYD15) fPAR product is produced at 500 m resolution and 8-day temporal fidelity from both NASA EOS Terra and Aqua sensor records.

Each HDF5 file contains metadata with Quality Assessment (QA) metadata flags that are set by the GMAO prior to delivery to National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). A separate metadata file with an .xml file extension is also delivered to NSIDC DAAC with the HDF5 file; it contains the same information as the HDF5 file-level metadata.

Quality control bit flags are provided in SPL4CMDL files to identify retrieval conditions including use of alternative ancillary data sets and exceedance of expected output field value ranges. Alternative ancillary conditions indicated in the QC bit flags include the use of alternative fPAR sources in place of baseline MODIS (MOD15) fPAR inputs, potential gaps in the SPL3SMA input stream, and instances where the ancillary fPAR 8-day climatology is used in place of the dynamic best QC MODIS fPAR input stream to estimate GPP. Expected PFT class specific range thresholds for each state variable (NEE, GPP, Rh, and SOC) have been established from dynamic algorithm simulations using long-term (10+ year) daily data input records from pre-launch data sources similar to those used for post-launch SPL4CMDL production, including MODIS (MOD15) fPAR, freeze-thaw status (Kim et al. 2012), and MERRA surface meteorology (Yi et al. 2011). These post-launch diagnostics are provided in SPL4CMDL files in the QA/carbon_model_bitflag data field for additional user evaluation. Table 7 indicates the bit-field positions for the above-described flags. A copy of Table 7 is also provided within each file as metadata for quick reference; refer to the QA/carbon_model_bitflag data field.

* When IsFill = 1, then all other bit fields will have value 1 and the entire uint16 integer will evaluate to 65534. Users should therefore check the value of IsFill prior to referencing other bit fields.

National Research Council (NRC). 2007. Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond (Executive Summary). National Academy of Sciences, National Academies Press, Washington DC. 1-35. http://www.nap.edu/catalog/11820.html.

FAQ

What are the latencies for SMAP radiometer data sets?

The following table describes both the required and actual latencies for the different SMAP radiometer data sets. Latency is defined as the time (# days, hh:mm:ss) from data acquisition to product generation.
Short name
Title
Latency
Required
Actual (mean1)
SPL1AP
SMAP L1A... read more

What data subsetting, reformatting, and reprojection services are available for SMAP data?

The following table describes the data subsetting, reformatting, and reprojection services that are currently available for SMAP data via the NASA Earthdata Search tool.
Short name
Title
Subsetting... read more

How To

How do I programmatically request data services such as subsetting, reformatting, and reprojection using an API?

The subsetting, reformatting, and reprojection services provided by NSIDC through NASA Earthdata Search can also be accessed programmatically as a synchronous REST interface. This programmatic access is provided via an HTTPS URL containing a series of... read more

The attached video tutorial provides step-by-step instructions on how to visualize SMAP data in Worldview (http://worldview.earthdata.nasa.gov/). NASA Worldview is a map-based application that allows you to interactively... read more

How do I search, order, and customize SMAP data using Earthdata Search?

This How To guide, relevent only to ESRI ArcMap10.5 and later versions, will outline the steps to follow in order to properly project and visualize global** SMAP L3 and L4 HDF data in ArcGIS. However, if you are running ArcMap10.4.1, there is a patch... read more

How do I access data using OPeNDAP?

Data can be programmatically accessed using NSIDC’s OPeNDAP Hyrax server, allowing you to reformat and subset data based on parameter and array index. For more information on OPeNDAP, including supported data sets and known issues, please see our OPeNDAP documentation: ... read more